We implement all the unconditional methods within the shared GANformer codebase [1]}, to ensures they are tested under comparable conditions in terms of training details, model sizes, and optimization scheme. For the conditional models, we use the authors' official implementations, likewise implemented as extensions to the Pix2PixHD repository for conditional generative modeling. All approaches have been trained with images of \(256 \times 256\)  resolution and data augmentation as detailed in section . See table REF  for the particular settings and hyperparameters of each model. The overall latent dimension is chosen based on performance among \(\lbrace 128, 256, 512\rbrace \) . The R1 regularization factor \(\gamma \)  is likewise chosen based on performance and training stability among \(\lbrace 1, 10, 20, 40, 80, 100\rbrace \) .
